March 2021 Summaries

Argo Rollouts is a progressive delivery controller designed for Kubernetes that facilitates deployments with minimal or zero downtime by using a gradual deployment method instead of an "all at once" approach. It enhances Kubernetes clusters by offering advanced deployment strategies like blue/green deployments, which allow for a second application instance to be tested without affecting live traffic. The key advantage of this method is the ability to quickly switch back to the previous version if issues arise, ensuring a seamless transition for users. Argo Rollouts also includes a "preview" service that enables testing of new versions before they become live, allowing developers to conduct health checks and run smoke tests. The article details a step-by-step guide to implementing blue/green deployments with Argo Rollouts, including utilizing Codefresh pipelines for automation. This process ensures that only versions passing integration tests reach production, highlighting the tool's capability to support continuous deployment with rollback options and other configurations.

The text discusses the benefits of using ephemeral Kubernetes clusters for continuous integration (CI) testing, highlighting the challenges and time consumption associated with traditional cluster creation and removal. It introduces "kind" (Kubernetes in Docker) as a faster alternative, capable of creating clusters in about two minutes and deleting them almost instantly, making it ideal for short-lived test environments. The discussion includes a practical guide on setting up a kind cluster within a Kubernetes pod using resources provided by Jie Yu and Steven Chung, and the steps to integrate this process into a Codefresh pipeline. The text further explains how to simplify this integration by using a "typed step" in Codefresh, which consolidates multiple steps into a reusable module, thus streamlining the pipeline setup and execution. The accompanying GitHub repository contains the necessary YAML configurations and examples to facilitate this setup.

Codefresh utilizes Figma for all design tasks, particularly focusing on creating flexible modals that allow for component reuse within their application design. The design strategy involves breaking down each modal into components, such as a header, content, and button bar, all organized within an auto-layout frame to ensure adaptability and efficiency. This approach allows components like icons and buttons to be easily swapped or adjusted in the design system, promoting consistency and ease of updates. The auto-layout feature is emphasized for its ability to dynamically shift elements, such as text and logos, ensuring a clean and responsive design. By creating components for modal content, the design process becomes streamlined and adaptable, allowing for quick updates and modifications without the need to redesign each element from scratch.

The blog outlines a step-by-step guide on how to utilize the Codefresh hybrid solution to execute CI/CD workflows on Kubernetes cluster resources, integrating the benefits of both SaaS and on-prem solutions. The Codefresh hybrid solution allows users to run workflows on their Kubernetes infrastructure while maintaining the security of their private resources. It offers a seamless way to integrate Git repositories and display them in the Codefresh UI, using Kubernetes secrets to ensure sensitive information remains within cluster boundaries. The approach balances security with the speed and flexibility of a SaaS platform, enabling access to private resources from an on-premises Git provider without compromising performance or security.

AWS Lambda is a serverless compute service that allows users to run code without the need to manage servers, making it a cost-effective and on-demand solution for various tasks, such as controlling Alexa Skills. This tutorial guides users through creating a basic "Hello World" AWS Lambda function and demonstrates how to deploy updates using a Codefresh pipeline. Initially, users can employ the AWS Console's Inline editor for simple tasks, but as projects grow in complexity, it is advisable to use advanced IDEs and development methodologies, including writing tests, to enhance code quality. The process involves creating an AWS Lambda function, setting up a GitHub repository, and linking it to a Codefresh account for seamless deployments. By establishing a robust release lifecycle process, developers can save time and efficiently detect issues, although it is essential to handle AWS keys securely via pipeline variables rather than embedding them directly in the code.